Autonomous Airborne Wireless Networks. Группа авторов

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1 Introduction

       Muhammad A. Imran, Oluwakayode Onireti, Shuja S. Ansari and Qammer H. Abbasi

       James Watt School of Engineering, University of Glasgow, Glasgow, UK

      Airborne networks (ANs) are now playing an increasingly crucial role in military, civilian, and public applications such as surveillance and monitoring, military, and rescue operations. More recently, airborne networks have also become a topic of interest in the industrial and research community of wireless communication. The 3rd Generation Partnership Project (3GPP) standardization has a study item devoted to facilitating the seamless integration of airborne wireless networks into future cellular networks. Airborne wireless networks enabled by unmanned aerial vehicles (UAVs) can provide cost‐effective and reliable wireless communications to support various use cases in future networks. Compared with high‐altitude platforms or conventional terrestrial communications, the provision of on‐demand communication systems with UAVs has faster deployment time and more flexibility in terms of reconfiguration. Further, UAV‐enabled propagation can also offer better communication channels due to the existence of the line‐of‐sight (LoS) links, which are of short range.

Despite the several benefits of airborne wireless networks, they suffer from some realistic constraints such as being energy constrained because of the limited battery power, safety concerns, and the strict flight zone. Hence, developing new signal processing, communication, and optimization framework for autonomous airborne wireless networks is essential. Such networks can offer high data rates and assist the traditional terrestrial networks to provide real‐time and ultrareliable sensing applications for the beyond‐5G networks. Achieving this gain requires the correct characterization of the propagation channel while considering the high mobility dynamics. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. The air‐to‐ground (AG) and the air‐to‐air (AA) channel propagation models for the airborne wireless network channel can be characterized by using measurement and empirical studies. Further, the key performance indicators (KPIs) of airborne wireless networks such as flight time, trajectory, data rate, energy efficiency, and latency need to be optimized for the different use cases.

      This book explores recent advances in the theory and practice of airborne wireless networks for the next generation of wireless networks to support various applications, including emergency communications, coverage and capacity expansion, Internet of things (IoT), information dissemination, future healthcare, pop‐up networks, etc. The book focuses on channel characteristics and modeling, networking architectures, self‐organized airborne networks, self‐organized backhaul, artificial‐intelligence‐enabled trajectory optimization, and application in sectors such as agriculture, underwater communications, and emergency networks. The book further highlights the main considerations during the design of the autonomous airborne networks and exploits new opportunities due to the recent advancement in wireless communication systems.

      This book for the first time evaluates the advances in the current state of the art and it provides readers with insights on how airborne wireless networks can seamlessly support various applications expected in future networks. More specifically, the book shows the readers how the integration of self‐organized networks and artificial intelligence can support the various use cases of airborne wireless networks.

      UAVs provide a suitable aerial platform for various wireless network applications that require reliable and ubiquitous communication. The channel model plays a crucial role in the wireless communications system and thus Chapter 2 focuses on the channel model for UAV networks. The authors first provide an overview of UAV networks in terms of their classification and how they can be used to enable future wireless communication systems. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of the end user to transfer data at higher rates. Hence, the authors discuss channel modeling in UAV communications while focusing on the salient feature of the AG and AA propagation channels. Finally, the chapter concludes by discussing some of the key research challenges for the practical deployment of UAVs as airborne wireless nodes.

In Chapter 3, the authors describe the fundamental properties of the ultrawide band (UWB) channel and present one of the first experimental off‐body studies between a human subject and an UAV at 7.5 GHz of bandwidth. In the study presented in this chapter, the transmitter antenna was placed on a UAV while the receiver antenna was patched on a human subject at different body locations during the campaign. The chapter presents the measurement setting, detailing the measurement campaign that was conducted in an indoor and an outdoor environment with LoS and non‐line‐of‐sight (NLoS) cases. Furthermore, the chapter presents the UWB‐unmanned aerial vehicle‐to‐wearables (UAV2W) channel characterization. Finally, the chapter presents the statistical analysis to determine the distribution that best characterizes the fading channels between different body locations and the UAV.

      Chapter 4 describes the use of a Q‐learning algorithm, which is based on a cooperative multiagent approach, to intelligently find the optimal position of a set of drones. The algorithm presented in the chapter is designed with the objective to minimize the number of users in an outage in the network. Hence, the algorithm determines the optimal distribution of frequencies and whether it should shut down a set of drones. The chapter also proposes and compares four different strategies for the Q‐learning algorithm СКАЧАТЬ